#ifndef CAFFE_RELU_LAYER_HPP_
#define CAFFE_RELU_LAYER_HPP_

#include <vector>
#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/layers/neuron_layer.hpp"


namespace caffe {

/* @brief Rectified Linear Unit non-linearity @f$ y = \max(0, x) @f$.
 *        The simple max is fast to compute, and the function does not saturate. */
template <typename Dtype>
class ReLULayer : public NeuronLayer<Dtype> {
 public:
  /* @param param provides ReLUParameter relu_param, with ReLULayer options:
   *   - negative_slope (\b optional, default 0). the value @f$ \nu @f$ by which negative values are multiplied. */
  explicit ReLULayer(const LayerParameter& param) : NeuronLayer<Dtype>(param) {}
  virtual inline const char* type() const { return "ReLU"; }

 protected:
  /* @param bottom input Blob vector (length 1) -# @f$ (N \times C \times H \times W) @f$ the inputs @f$ x @f$
   * @param top output Blob vector (length 1)
   *   -# @f$ (N \times C \times H \times W) @f$ the computed outputs @f$ y = \max(0, x)
   *      @f$ by default.  If a non-zero negative_slope @f$ \nu @f$ is provided,
   *      the computed outputs are @f$ y = \max(0, x) + \nu \min(0, x) @f$. */
  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);

  /* @brief Computes the error gradient w.r.t. the ReLU inputs.
   * @param top output Blob vector (length 1), providing the error gradient with respect to the outputs
   *   -# @f$ (N \times C \times H \times W) @f$ containing error gradients @f$ \frac{\partial E}{\partial y} @f$
   *      with respect to computed outputs @f$ y @f$
   * @param propagate_down see Layer::Backward.
   * @param bottom input Blob vector (length 1)
   *   -# @f$ (N \times C \times H \times W) @f$ the inputs @f$ x @f$; Backward fills their diff with
   *      gradients @f$ \frac{\partial E}{\partial x} = \left\{
   *        \begin{array}{lr} 0 & \mathrm{if} \; x \le 0 \\
   *            \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0
   *        \end{array} \right.
   *      @f$ if propagate_down[0], by default.
   *      If a non-zero negative_slope @f$ \nu @f$ is provided,
   *      the computed gradients are @f$
   *        \frac{\partial E}{\partial x} = \left\{
   *        \begin{array}{lr}
   *            \nu \frac{\partial E}{\partial y} & \mathrm{if} \; x \le 0 \\
   *            \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0
   *        \end{array} \right. @f$. */
  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
};

}  // namespace caffe
#endif  // CAFFE_RELU_LAYER_HPP_
